Unearthing Optimal Symbiotic Rhizobia Partners from the Main Production Area of Phaseolus vulgaris in Yunnan.

Phaseolus vulgaris is a globally important legume cash crop, which can carry out symbiotic nitrogen fixation with rhizobia. The presence of suitable rhizobia in cultivating soils is crucial for legume cropping, especially in areas beyond the plant-host native range, where soils may lack efficient symbiotic partners. We analyzed the distribution patterns and traits of native rhizobia associated with P. vulgaris in soils of Yunnan, where the common bean experienced a recent expansion. A total of 608 rhizobial isolates were tracked from soils of fifteen sampling sites using two local varieties of P. vulgaris. The isolates were discriminated into 43 genotypes as defined by IGS PCR-RFLP. Multiple locus sequence analysis based on recA, atpD and rpoB of representative strains placed them into 11 rhizobial species of Rhizobium involving Rhizobium sophorae, Rhizobium acidisoli, Rhizobium ecuadorense, Rhizobium hidalgonense, Rhizobium vallis, Rhizobium sophoriradicis, Rhizobium croatiense, Rhizobium anhuiense, Rhizobium phaseoli, Rhizobium chutanense and Rhizobium etli, and five unknown Rhizobium species; Rhizobium genosp. I~V. R. phaseoli and R. anhuiense were the dominant species (28.0% and 28.8%) most widely distributed, followed by R. croatiense (14.8%). The other rhizobial species were less numerous or site-specific. Phylogenies of nodC and nifH markers, were divided into two specific symbiovars, sv. phaseoli regardless of the species affiliation and sv. viciae associated with R. vallis. Through symbiotic effect assessment, all the tested strains nodulated both P. vulgaris varieties, often resulting with a significant greenness index (91-98%). However, about half of them exhibited better plant biomass performance, at least on one common bean variety, and two isolates (CYAH-6 and BLYH-15) showed a better symbiotic efficiency score. Representative strains revealed diverse abiotic stress tolerance to NaCl, acidity, alkalinity, temperature, drought and glyphosate. One strain efficient on both varieties and exhibiting stress abiotic tolerance (BLYH-15) belonged to R. genosp. IV sv. phaseoli, a species first found as a legume symbiont.

Higher genetically predicted triglyceride level increases the bladder cancer risk independent of LDL and HDL levels.

The causal relationship between lipid levels and bladder cancer is still inconclusive currently. We aimed to reveal the causal relationship between triglycerides, HDL, and LDL and the risk of bladder cancer by univariable and multivariable Mendelian randomization (MR) analysis. The single nucleotide polymorphisms (SNPs) of exposure (triglycerides: 441,016 samples; HDL: 403,943 samples; LDL: 440,546 samples) were obtained from UK Biobank. The Genetic variation related to bladder cancer included 1554 cases and 359,640 controls. Univariable and multivariable MR methods were conducted with subsequent analysis, and smoking was regarded as a confounder. The inverse-variance weighted (IVW), MR-Egger, weighted-median method, Cochran's Q test, and MR-PRESSO were considered the main MR analysis and sensitivity analysis methods. Univariable MR analysis results suggested the triglycerides level (P = 0.011, OR = 1.001, 95% CI = 1.000-1.002) was causally associated with increased risk of bladder cancer. Multivariable MR results indicated that higher triglyceride levels could still increase the risk of bladder cancer after adjusting the effects of HDL, LDL, and smoking (P = 0.042, OR = 1.001, 95% CI = 1.000-1.002). Our findings supported that triglyceride level is causally associated with an increased risk of bladder cancer independent of LDL and HDL at the genetic level. Timely attention to changes in blood lipid levels might reduce the risk of bladder cancer.

Natural Language Processing-Powered Real-Time Monitoring Solution for Vaccine Sentiments and Hesitancy on Social Media: System Development and Validation.

BACKGROUND: Vaccines serve as a crucial public health tool, although vaccine hesitancy continues to pose a significant threat to full vaccine uptake and, consequently, community health. Understanding and tracking vaccine hesitancy is essential for effective public health interventions; however, traditional survey methods present various limitations. OBJECTIVE: This study aimed to create a real-time, natural language processing (NLP)-based tool to assess vaccine sentiment and hesitancy across 3 prominent social media platforms. METHODS: We mined and curated discussions in English from Twitter (subsequently rebranded as X), Reddit, and YouTube social media platforms posted between January 1, 2011, and October 31, 2021, concerning human papillomavirus; measles, mumps, and rubella; and unspecified vaccines. We tested multiple NLP algorithms to classify vaccine sentiment into positive, neutral, or negative and to classify vaccine hesitancy using the World Health Organization's (WHO) 3Cs (confidence, complacency, and convenience) hesitancy model, conceptualizing an online dashboard to illustrate and contextualize trends. RESULTS: We compiled over 86 million discussions. Our top-performing NLP models displayed accuracies ranging from 0.51 to 0.78 for sentiment classification and from 0.69 to 0.91 for hesitancy classification. Explorative analysis on our platform highlighted variations in online activity about vaccine sentiment and hesitancy, suggesting unique patterns for different vaccines. CONCLUSIONS: Our innovative system performs real-time analysis of sentiment and hesitancy on 3 vaccine topics across major social networks, providing crucial trend insights to assist campaigns aimed at enhancing vaccine uptake and public health.

Spatially distributed cytokinins: Metabolism, signaling, and transport.

Cytokinins are mobile phytohormones that regulate plant growth, development, and environmental adaptability. The major cytokinin species include isopentenyl adenine (iP), trans-zeatin (tZ), cis-zeatin (cZ), and dihydrozeatin (DZ). The spatial distributions of different cytokinin species in different organelles, cells, tissues, and organs are primarily shaped by biosynthesis via isopentenyltransferases (IPT), cytochrome P450 monooxygenase, and 5'-ribonucleotide phosphohydrolase and by conjugation or catabolism via glycosyltransferase or cytokinin oxidase/dehydrogenase. Cytokinins bind to histidine receptor kinases in the endoplasmic reticulum or plasma membrane and relay signals to response regulators in the nucleus via shuttle proteins known as histidine phosphotransfer proteins. The movements of cytokinins from sites of biosynthesis to sites of signal perception usually require long-distance, intercellular, and intracellular transport. In the past decade, ATP-binding cassette (ABC) transporters, purine permeases (PUP), AZA-GUANINE RESISTANT (AZG) transporters, equilibrative nucleoside transporters (ENT), and Sugars Will Eventually Be Exported transporters (SWEET) have been characterized as involved in cytokinin transport processes. This review begins by introducing the spatial distributions of various cytokinins and the subcellular localizations of the proteins involved in their metabolism and signaling. Highlights focus on an inventory of the characterized transporters involved in cytokinin compartmentalization, including long-distance, intercellular, and intracellular transport, and the regulation of the spatial distributions of cytokinins by environmental cues. Future directions for cytokinin research are also discussed.

SEETrials: Leveraging Large Language Models for Safety and Efficacy Extraction in Oncology Clinical Trials.

Background: Initial insights into oncology clinical trial outcomes are often gleaned manually from conference abstracts. We aimed to develop an automated system to extract safety and efficacy information from study abstracts with high precision and fine granularity, transforming them into computable data for timely clinical decision-making. Methods: We collected clinical trial abstracts from key conferences and PubMed (2012-2023). The SEETrials system was developed with four modules: preprocessing, prompt modeling, knowledge ingestion and postprocessing. We evaluated the system's performance qualitatively and quantitatively and assessed its generalizability across different cancer types- multiple myeloma (MM), breast, lung, lymphoma, and leukemia. Furthermore, the efficacy and safety of innovative therapies, including CAR-T, bispecific antibodies, and antibody-drug conjugates (ADC), in MM were analyzed across a large scale of clinical trial studies. Results: SEETrials achieved high precision (0.958), recall (sensitivity) (0.944), and F1 score (0.951) across 70 data elements present in the MM trial studies Generalizability tests on four additional cancers yielded precision, recall, and F1 scores within the 0.966-0.986 range. Variation in the distribution of safety and efficacy-related entities was observed across diverse therapies, with certain adverse events more common in specific treatments. Comparative performance analysis using overall response rate (ORR) and complete response (CR) highlighted differences among therapies: CAR-T (ORR: 88%, 95% CI: 84-92%; CR: 95%, 95% CI: 53-66%), bispecific antibodies (ORR: 64%, 95% CI: 55-73%; CR: 27%, 95% CI: 16-37%), and ADC (ORR: 51%, 95% CI: 37-65%; CR: 26%, 95% CI: 1-51%). Notable study heterogeneity was identified (>75% I 2 heterogeneity index scores) across several outcome entities analyzed within therapy subgroups. Conclusion: SEETrials demonstrated highly accurate data extraction and versatility across different therapeutics and various cancer domains. Its automated processing of large datasets facilitates nuanced data comparisons, promoting the swift and effective dissemination of clinical insights.

Machine learning models for abstract screening task - A systematic literature review application for health economics and outcome research.

OBJECTIVE: Systematic literature reviews (SLRs) are critical for life-science research. However, the manual selection and retrieval of relevant publications can be a time-consuming process. This study aims to (1) develop two disease-specific annotated corpora, one for human papillomavirus (HPV) associated diseases and the other for pneumococcal-associated pediatric diseases (PAPD), and (2) optimize machine- and deep-learning models to facilitate automation of the SLR abstract screening. METHODS: This study constructed two disease-specific SLR screening corpora for HPV and PAPD, which contained citation metadata and corresponding abstracts. Performance was evaluated using precision, recall, accuracy, and F1-score of multiple combinations of machine- and deep-learning algorithms and features such as keywords and MeSH terms. RESULTS AND CONCLUSIONS: The HPV corpus contained 1697 entries, with 538 relevant and 1159 irrelevant articles. The PAPD corpus included 2865 entries, with 711 relevant and 2154 irrelevant articles. Adding additional features beyond title and abstract improved the performance (measured in Accuracy) of machine learning models by 3% for HPV corpus and 2% for PAPD corpus. Transformer-based deep learning models that consistently outperformed conventional machine learning algorithms, highlighting the strength of domain-specific pre-trained language models for SLR abstract screening. This study provides a foundation for the development of more intelligent SLR systems.

Prompt Tuning in Biomedical Relation Extraction.

Biomedical relation extraction (RE) is critical in constructing high-quality knowledge graphs and databases as well as supporting many downstream text mining applications. This paper explores prompt tuning on biomedical RE and its few-shot scenarios, aiming to propose a simple yet effective model for this specific task. Prompt tuning reformulates natural language processing (NLP) downstream tasks into masked language problems by embedding specific text prompts into the original input, facilitating the adaption of pre-trained language models (PLMs) to better address these tasks. This study presents a customized prompt tuning model designed explicitly for biomedical RE, including its applicability in few-shot learning contexts. The model's performance was rigorously assessed using the chemical-protein relation (CHEMPROT) dataset from BioCreative VI and the drug-drug interaction (DDI) dataset from SemEval-2013, showcasing its superior performance over conventional fine-tuned PLMs across both datasets, encompassing few-shot scenarios. This observation underscores the effectiveness of prompt tuning in enhancing the capabilities of conventional PLMs, though the extent of enhancement may vary by specific model. Additionally, the model demonstrated a harmonious balance between simplicity and efficiency, matching state-of-the-art performance without needing external knowledge or extra computational resources. The pivotal contribution of our study is the development of a suitably designed prompt tuning model, highlighting prompt tuning's effectiveness in biomedical RE. It offers a robust, efficient approach to the field's challenges and represents a significant advancement in extracting complex relations from biomedical texts. Supplementary Information: The online version contains supplementary material available at 10.1007/s41666-024-00162-9.

Enhancing Early Detection of Cognitive Decline in the Elderly: A Comparative Study Utilizing Large Language Models in Clinical Notes.

Background: Large language models (LLMs) have shown promising performance in various healthcare domains, but their effectiveness in identifying specific clinical conditions in real medical records is less explored. This study evaluates LLMs for detecting signs of cognitive decline in real electronic health record (EHR) clinical notes, comparing their error profiles with traditional models. The insights gained will inform strategies for performance enhancement. Methods: This study, conducted at Mass General Brigham in Boston, MA, analyzed clinical notes from the four years prior to a 2019 diagnosis of mild cognitive impairment in patients aged 50 and older. We used a randomly annotated sample of 4,949 note sections, filtered with keywords related to cognitive functions, for model development. For testing, a random annotated sample of 1,996 note sections without keyword filtering was utilized. We developed prompts for two LLMs, Llama 2 and GPT-4, on HIPAA-compliant cloud-computing platforms using multiple approaches (e.g., both hard and soft prompting and error analysis-based instructions) to select the optimal LLM-based method. Baseline models included a hierarchical attention-based neural network and XGBoost. Subsequently, we constructed an ensemble of the three models using a majority vote approach. Results: GPT-4 demonstrated superior accuracy and efficiency compared to Llama 2, but did not outperform traditional models. The ensemble model outperformed the individual models, achieving a precision of 90.3%, a recall of 94.2%, and an F1-score of 92.2%. Notably, the ensemble model showed a significant improvement in precision, increasing from a range of 70%-79% to above 90%, compared to the best-performing single model. Error analysis revealed that 63 samples were incorrectly predicted by at least one model; however, only 2 cases (3.2%) were mutual errors across all models, indicating diverse error profiles among them. Conclusions: LLMs and traditional machine learning models trained using local EHR data exhibited diverse error profiles. The ensemble of these models was found to be complementary, enhancing diagnostic performance. Future research should investigate integrating LLMs with smaller, localized models and incorporating medical data and domain knowledge to enhance performance on specific tasks.

A comprehensive transformer-based approach for high-accuracy gas adsorption predictions in metal-organic frameworks.

Gas separation is crucial for industrial production and environmental protection, with metal-organic frameworks (MOFs) offering a promising solution due to their tunable structural properties and chemical compositions. Traditional simulation approaches, such as molecular dynamics, are complex and computationally demanding. Although feature engineering-based machine learning methods perform better, they are susceptible to overfitting because of limited labeled data. Furthermore, these methods are typically designed for single tasks, such as predicting gas adsorption capacity under specific conditions, which restricts the utilization of comprehensive datasets including all adsorption capacities. To address these challenges, we propose Uni-MOF, an innovative framework for large-scale, three-dimensional MOF representation learning, designed for multi-purpose gas prediction. Specifically, Uni-MOF serves as a versatile gas adsorption estimator for MOF materials, employing pure three-dimensional representations learned from over 631,000 collected MOF and COF structures. Our experimental results show that Uni-MOF can automatically extract structural representations and predict adsorption capacities under various operating conditions using a single model. For simulated data, Uni-MOF exhibits remarkably high predictive accuracy across all datasets. Additionally, the values predicted by Uni-MOF correspond with the outcomes of adsorption experiments. Furthermore, Uni-MOF demonstrates considerable potential for broad applicability in predicting a wide array of other properties.

Genetic prediction of modifiable lifestyle factors for erectile dysfunction.

Background: The causal relationship between certain lifestyle factors and erectile dysfunction (ED) is still uncertain. Aim: The study sought to investigate the causal effect of 9 life factors on ED through 2-sample single-variable Mendelian randomization (SVMR) and multivariable Mendelian randomization (MVMR). Methods: Genetic instruments to proxy 9 risk factors were identified by genome-wide association studies. The genome-wide association studies estimated the connection of these genetic variants with ED risk (n = 223 805). We conducted SVMR, inverse variance-weighting, Cochran's Q, weighted median, MR-Egger, MR-PRESSO (Mendelian Randomization Pleiotropy RESidual Sum and Outlier), and MVMR analyses to explore the total and direct relationship between life factors and ED. Outcomes: The primary outcome was defined as self or physician-reported ED, or using oral ED medication, or a history of surgery related to ED. Results: In SVMR analyses, suggestive associations with increased the risk of ED were noted for ever smoked (odds ratio [OR], 5.894; 95% confidence interval [CI], 0.469 to 3.079; P = .008), alcohol consumption (OR, 1.495; 95% CI, 0.044 to 0.760; P = .028) and body mass index (BMI) (OR, 1.177; 95% CI, 0.057 to 0.268; P = .003). Earlier age at first intercourse was significantly related to reduced ED risk (OR, 0.659; 95% CI, -0.592 to -0.244; P = 2.5 × 10-6). No strong evidence was found for the effect of coffee intake, time spent driving, physical activity, and leisure sedentary behaviors on the incidence of ED (All P > .05). The result of MVMR analysis for BMI (OR, 1.13; 95% CI, 1.01 to 1.25; P = .045) and earlier age at first intercourse (OR, 0.77; 95% CI, 0.56 to 0.99; P = .018) provided suggestive evidence for the direct impact on ED, while no causal factor was detected for alcoholic drinks per week and ever smoked. Clinical implications: This study provides evidence for the impact of certain modifiable lifestyle factors on the development of ED. Strengths and limitations: We performed both SVMR and MVMR to strengthen the causal relationship between exposures and outcomes. However, the population in this study was limited to European ancestry. Conclusion: Ever smoked, alcoholic drinks per week, BMI, and age first had sexual intercourse were causally related to ED, while the potential connection between coffee intake, physical activity, recreational sedentary habits, and increased risk of ED needs to be further confirmed.

Spatial-Temporal Mapping Reveals the Golgi as the Major Processing Site for the Pathogenic Swedish APP Mutation: Familial APP Mutant Shifts the Major APP Processing Site.

Alzheimer's disease is associated with increased levels of amyloid beta (Aß) generated by sequential intracellular cleavage of amyloid precursor protein (APP) by membrane-bound secretases. However, the spatial and temporal APP cleavage events along the trafficking pathways are poorly defined. Here, we use the Retention Using Selective Hooks (RUSH) to compare in real time the anterograde trafficking and temporal cleavage events of wild-type APP (APPwt) with the pathogenic Swedish APP (APPswe) and the disease-protective Icelandic APP (APPice). The analyses revealed differences in the trafficking profiles and processing between APPwt and the APP familial mutations. While APPwt was predominantly processed by the ß-secretase, BACE1, following Golgi transport to the early endosomes, the transit of APPswe through the Golgi was prolonged and associated with enhanced amyloidogenic APP processing and Aß secretion. A 20°C block in cargo exit from the Golgi confirmed ß- and γ-secretase processing of APPswe in the Golgi. Inhibition of the ß-secretase, BACE1, restored APPswe anterograde trafficking profile to that of APPwt. APPice was transported rapidly through the Golgi to the early endosomes with low levels of Aß production. This study has revealed different intracellular locations for the preferential cleavage of APPwt and APPswe and Aß production, and the Golgi as the major processing site for APPswe, findings relevant to understand the molecular basis of Alzheimer's disease.

Role of hydrocolloid dressing in preventing face pressure ulcers from non-invasive ventilation and facilitating postoperative wound healing in maxillofacial surgery: A meta-analysis.

Facial pressure ulcers from non-invasive ventilation (NIV) and challenges in wound healing post-maxillofacial surgery are significant concerns in clinical care. This meta-analysis aimed to evaluate the effectiveness of hydrocolloid dressings in these contexts. From a pool of 1135 articles, 8 studies met the inclusion criteria. Hydrocolloid dressings demonstrated a significant reduction in facial pressure ulcers for NIV patients, with lower REEDA scores 1-week postapplication (standardized mean difference [SMD] = -16.7, 95% confidence interval [CI]: -24.26 to -9.15, p < 0.01). In maxillofacial surgery, patients treated with hydrocolloid dressings exhibited improved wound healing and reduced scar formation, evidenced by lower Manchester Scar Scale scores 3 months post-surgery (SMD = -15.46, 95% CI: -20.28 to -10.64, p < 0.01). These findings suggest that hydrocolloid dressings are effective in both preventing NIV-related facial pressure ulcers and enhancing wound healing in maxillofacial surgery.

Chickpea: Its Origin, Distribution, Nutrition, Benefits, Breeding, and Symbiotic Relationship with Mesorhizobium Species.

Chickpea (Cicer arietinum L.), encompassing the desi and kabuli varieties, is a beloved pulse crop globally. Its cultivation spans over fifty countries, from the Indian subcontinent and southern Europe to the Middle East, North Africa, the Americas, Australia, and China. With a rich composition of carbohydrates and protein, constituting 80% of its dry seed mass, chickpea is also touted for its numerous health benefits, earning it the title of a 'functional food'. In the past two decades, research has extensively explored the rhizobial diversity associated with chickpea and its breeding in various countries across Europe, Asia, and Oceania, aiming to understand its impact on the sustainable yield and quality of chickpea crops. To date, four notable species of Mesorhizobium-M. ciceri, M. mediterraneum, M. muleiense, and M. wenxiniae-have been reported, originally isolated from chickpea root nodules. Other species, such as M. amorphae, M. loti, M. tianshanense, M. oportunistum, M. abyssinicae, and M. shonense, have been identified as potential symbionts of chickpea, possibly acquiring symbiotic genes through lateral gene transfer. While M. ciceri and M. mediterraneum are widely distributed and studied across chickpea-growing regions, they remain absent in China, where M. muleiense and M. wenxiniae are the sole rhizobial species associated with chickpea. The geographic distribution of chickpea rhizobia is believed to be influenced by factors such as genetic characteristics, competitiveness, evolutionary adaptation to local soil conditions, and compatibility with native soil microbes. Inoculating chickpea with suitable rhizobial strains is crucial when introducing the crop to new regions lacking indigenous chickpea rhizobia. The introduction of a novel chickpea variety, coupled with the effective use of rhizobia for inoculation, offers the potential not only to boost the yield and seed quality of chickpeas, but also to enhance crop productivity within rotation and intercropped systems involving chickpea and other crops. Consequently, this advancement holds the promise to drive forward the cause of sustainable agriculture on a global scale.

Abscisic acid root-to-shoot translocation by transporter AtABCG25 mediates stomatal movements in Arabidopsis.

The phytohormone abscisic acid (ABA) plays a central role in regulating stomatal movements under drought conditions. The root-derived peptide CLAVATA3/EMBRYO SURROUNDING REGION-RELATED 25 (CLE25) moves from the root to shoot for activating ABA biosynthesis under drought conditions. However, the root-to-shoot translocation of root-derived ABA and its regulation of stomatal movements in the shoot remain to be clarified. Here, we reveal that the ABA transporter ATP-binding cassette subfamily G member 25 (AtABCG25) mediates root-to-shoot translocation of ABA and ABA-glucosyl ester (ABA-GE) in Arabidopsis (Arabidopsis thaliana). Isotope-labeled ABA tracer experiments and hormone quantification in xylem sap showed that the root-to-shoot translocation of ABA and ABA-GE was substantially impaired in the atabcg25 mutant under nondrought and drought conditions. However, the contents of ABA and ABA-GE in the leaves were lower in the atabcg25 mutant than in the wild type (WT) under nondrought but similar under drought conditions. Consistently, the stomatal closure was suppressed in the atabcg25 mutant under nondrought but not under drought conditions. The transporter activity assays showed that AtABCG25 directly exported ABA and ABA-GE in planta and in yeast (Saccharomyces cerevisiae) cells. Thus, we proposed a working model in which root-derived ABA transported by AtABCG25 via xylem mediates stomatal movements in the shoot under nondrought conditions but might exhibit little effect on stomatal movements under drought conditions. These findings extend the functions of AtABCG25 and provide insights into the long-distance translocation of ABA and its role in stomatal movements.

The chromatin remodeler ERCC6 and the histone chaperone NAP1 are involved in apurinic/apyrimidinic endonuclease-mediated DNA repair.

During base excision repair (BER), the apurinic or apyrimidinic (AP) site serves as an intermediate product following base excision. In plants, APE-redox protein (ARP) represents the major AP site of cleavage activity. Despite the well-established understanding that the nucleosomal structure acts as a barrier to various DNA-templated processes, the regulatory mechanisms underlying BER at the chromatin level remain elusive, especially in plants. In this study, we identified plant chromatin remodeler Excision Repair Cross-Complementing protein group 6 (ERCC6) and histone chaperone Nucleosome Assembly Protein 1 (NAP1) as interacting proteins with ARP. The catalytic ATPase domain of ERCC6 facilitates its interaction with both ARP and NAP1. Additionally, ERCC6 and NAP1 synergistically contribute to nucleosome sliding and exposure of hindered endonuclease cleavage sites. Loss-of-function mutations in Arabidopsis (Arabidopsis thaliana) ERCC6 or NAP1 resulted in arp-dependent plant hypersensitivity to 5-fluorouracil, a toxic agent inducing BER, and the accumulation of AP sites. Furthermore, similar protein interactions are also found in yeast cells, suggesting a conserved recruitment mechanism employed by the AP endonuclease to overcome chromatin barriers during BER progression.

Characterization and optimization of 5´ untranslated region containing poly-adenine tracts in Kluyveromyces marxianus using machine-learning model.

BACKGROUND: The 5´ untranslated region (5´ UTR) plays a key role in regulating translation efficiency and mRNA stability, making it a favored target in genetic engineering and synthetic biology. A common feature found in the 5´ UTR is the poly-adenine (poly(A)) tract. However, the effect of 5´ UTR poly(A) on protein production remains controversial. Machine-learning models are powerful tools for explaining the complex contributions of features, but models incorporating features of 5´ UTR poly(A) are currently lacking. Thus, our goal is to construct such a model, using natural 5´ UTRs from Kluyveromyces marxianus, a promising cell factory for producing heterologous proteins. RESULTS: We constructed a mini-library consisting of 207 5´ UTRs harboring poly(A) and 34 5´ UTRs without poly(A) from K. marxianus. The effects of each 5´ UTR on the production of a GFP reporter were evaluated individually in vivo, and the resulting protein abundance spanned an approximately 450-fold range throughout. The data were used to train a multi-layer perceptron neural network (MLP-NN) model that incorporated the length and position of poly(A) as features. The model exhibited good performance in predicting protein abundance (average R2 = 0.7290). The model suggests that the length of poly(A) is negatively correlated with protein production, whereas poly(A) located between 10 and 30 nt upstream of the start codon (AUG) exhibits a weak positive effect on protein abundance. Using the model as guidance, the deletion or reduction of poly(A) upstream of 30 nt preceding AUG tended to improve the production of GFP and a feruloyl esterase. Deletions of poly(A) showed inconsistent effects on mRNA levels, suggesting that poly(A) represses protein production either with or without reducing mRNA levels. CONCLUSION: The effects of poly(A) on protein production depend on its length and position. Integrating poly(A) features into machine-learning models improves simulation accuracy. Deleting or reducing poly(A) upstream of 30 nt preceding AUG tends to enhance protein production. This optimization strategy can be applied to enhance the yield of K. marxianus and other microbial cell factories.

Teriflunomide Promotes Blood-Brain Barrier Integrity by Upregulating Claudin-1 via the Wnt/ß-catenin Signaling Pathway in Multiple Sclerosis.

The blood-brain barrier (BBB) and tight junction (TJ) proteins maintain the homeostasis of the central nervous system (CNS). The dysfunction of BBB allows peripheral T cells infiltration into CNS and contributes to the pathophysiology of multiple sclerosis (MS). Teriflunomide is an approved drug for the treatment of MS by suppressing lymphocytes proliferation. However, whether teriflunomide has a protective effect on BBB in MS is not understood. We found that teriflunomide restored the injured BBB in the EAE model. Furthermore, teriflunomide treatment over 6 months improved BBB permeability and reduced peripheral leakage of CNS proteins in MS patients. Teriflunomide increased human brain microvascular endothelial cell (HBMEC) viability and promoted BBB integrity in an in vitro cell model. The TJ protein claudin-1 was upregulated by teriflunomide and responsible for the protective effect on BBB. Furthermore, RNA sequencing revealed that the Wnt signaling pathway was affected by teriflunomide. The activation of Wnt signaling pathway increased claudin-1 expression and reduced BBB damage in cell model and EAE rats. Our study demonstrated that teriflunomide upregulated the expression of the tight junction protein claudin-1 in endothelial cells and promoted the integrity of BBB through Wnt signaling pathway.

AutoCriteria: a generalizable clinical trial eligibility criteria extraction system powered by large language models.

OBJECTIVES: We aim to build a generalizable information extraction system leveraging large language models to extract granular eligibility criteria information for diverse diseases from free text clinical trial protocol documents. We investigate the model's capability to extract criteria entities along with contextual attributes including values, temporality, and modifiers and present the strengths and limitations of this system. MATERIALS AND METHODS: The clinical trial data were acquired from https://ClinicalTrials.gov/. We developed a system, AutoCriteria, which comprises the following modules: preprocessing, knowledge ingestion, prompt modeling based on GPT, postprocessing, and interim evaluation. The final system evaluation was performed, both quantitatively and qualitatively, on 180 manually annotated trials encompassing 9 diseases. RESULTS: AutoCriteria achieves an overall F1 score of 89.42 across all 9 diseases in extracting the criteria entities, with the highest being 95.44 for nonalcoholic steatohepatitis and the lowest of 84.10 for breast cancer. Its overall accuracy is 78.95% in identifying all contextual information across all diseases. Our thematic analysis indicated accurate logic interpretation of criteria as one of the strengths and overlooking/neglecting the main criteria as one of the weaknesses of AutoCriteria. DISCUSSION: AutoCriteria demonstrates strong potential to extract granular eligibility criteria information from trial documents without requiring manual annotations. The prompts developed for AutoCriteria generalize well across different disease areas. Our evaluation suggests that the system handles complex scenarios including multiple arm conditions and logics. CONCLUSION: AutoCriteria currently encompasses a diverse range of diseases and holds potential to extend to more in the future. This signifies a generalizable and scalable solution, poised to address the complexities of clinical trial application in real-world settings.

Quasi-Solid-State All-V2O5 Battery.

Solid-state symmetrical battery represents a promising paradigm for future battery technology. However, its development is hindered by the deficiency of high-performance bipolar electrodes and compatible solid electrolytes. Herein, a quasi-solid-state all-V2O5 battery constructed by a binder-free carbon fabric-V2O5 nanowires@graphene (CVOG) bipolar electrode and a softly cross-linked polyethylene oxide-based solid polymer electrolyte (SPE) is reported. The synergetic effect of nano-structuring of V2O5, hierarchical conductive network, and graphene wrapping endows the CVOG electrode with boosted reaction kinetics and suppressed vanadium dissolution. The cathodic and anodic reactions of CVOG are decoupled by electrochemical analysis, conceiving the feasibility of constructing all-V2O5 full battery. In manifesting the solid-state all-V2O5 battery, the robust and elastic SPE exhibits high ionic conductivity, tight/self-adaptable electrolyte-electrode contact, and a low charge-transfer barrier. The resultant solid-state full battery exhibits a high reversible capacity of 158 mAh g-1 at 0.1 C, good capacity retention of over 61% from 0.1 C to 2 C, and remarkable cycling stability of 77% capacity retention after 1000 cycles at 1 C, which surpass other solid-state symmetrical batteries. Hence, this work provides a practice of high-performance solid-state batteries with symmetrical configuration and is constructive for next-generation battery technology.

Long-term live cell imaging during differentiation of human iPSC-derived neurons.

Live-cell imaging is crucial to appreciate the dynamics and the complexity of cellular interaction processes. However, live-cell imaging of human neurons is challenging due to neuronal sensitivity. Here, we describe a long-term live-cell imaging protocol for neurons derived from human induced pluripotent stem cells. By using an IncuCyte live-cell imaging system, we have obtained information on neuronal dynamics during the different stages of neurogenesis. The protocol has also been developed to monitor the dynamics of the neuronal intracellular organelles. For complete details on the use and execution of this protocol, please refer to Wang et al.1.